{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0b9ffbb2-8d33-eb0c-5a42-0733bdc3b74b",
    "_uuid": "e14f65723f5ac826104e861c45e58525740d8b2a"
   },
   "source": [
    "# Bikeshare数据集上的特征工程\n",
    "\n",
    "1、\t任务描述\n",
    "请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。根据每天的天气信息，预测该天的单车共享骑行量。\n",
    "\n",
    "原始数据集地址：http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset\n",
    "1)\t文件说明\n",
    "day.csv: 按天计的单车共享次数（作业只需使用该文件）\n",
    "hour.csv: 按小时计的单车共享次数（无需理会）\n",
    "readme：数据说明文件\n",
    "\n",
    "2)\t字段说明\n",
    "Instant记录号\n",
    "Dteday：日期\n",
    "Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "yr：年份，(0: 2011, 1:2012)\n",
    "mnth：月份( 1 to 12)\n",
    "hr：小时 (0 to 23)  （只在hour.csv有，作业忽略此字段）\n",
    "holiday：是否是节假日\n",
    "weekday：星期中的哪天，取值为0～6\n",
    "workingday：是否工作日\n",
    "1=工作日 （是否为工作日，1为工作日，0为非周末或节假日\n",
    "weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）\n",
    "temp：气温摄氏度\n",
    "atemp：体感温度\n",
    "hum：湿度\n",
    "windspeed：风速\n",
    "casual：非注册用户个数\n",
    "registered：注册用户个数\n",
    "cnt：给定日期（天）时间（每小时）总租车人数，响应变量y （cnt = casual + registered）\n",
    "\n",
    "casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "2ba3154a-c2aa-3158-1984-63ad2c0c786a",
    "_uuid": "5eb696b95780825e94ddb49787f9fa339fc3833b"
   },
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入数据\n",
    "\n",
    "数据预处理对训练数据和测试数据需进行同样处理，因此将二者一起读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "21fa35be-878b-b4f2-ef6e-68dc070b8bfa",
    "_uuid": "73aee228226be55c0c8a6e4fcbf818c56cd94926"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n",
    "#print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "be11c1c7-acfe-cb9a-bc81-95d461b884c5",
    "_uuid": "b53750ea6a3a2a02aa4297f5401e29c2f4d92e31"
   },
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据值型特征，用常用统计量观察其分布\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      " mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      " weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      " weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print ('\\n %s属性的不同取值和出现的次数'%col)\n",
    "    print (train[col].value_counts())\n",
    "    train[col] = train[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类别型特征的取值不多\n",
    "类别型特征可以采用独热编码（One hot encoding）/哑编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 每年的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0cbc45a90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['yr',\n",
    "                          'cnt']],\n",
    "              x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011年和2012年的分布差异很大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一年中每天的骑车量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'dayly distribution of counts')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "train['date'] = pd.to_datetime(train['dteday'])\n",
    "train['dayofyear'] = train[\"date\"].dt.dayofyear  #减今年的第几天\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['dayofyear',\n",
    "                           'cnt',\n",
    "                           'yr']],\n",
    "             x='dayofyear',y='cnt',\n",
    "             hue='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相邻天的骑车数目好像不是特别连续，可以不考虑增加时序特征（y_(t-1) - y_(t-2)...）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 季节与骑车数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['season',\n",
    "                       'cnt']],\n",
    "           x=\"season\",y=\"cnt\")\n",
    "ax.set(title=\"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 月份与骑车数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['mnth',\n",
    "                       'cnt']],\n",
    "           x=\"mnth\",y=\"cnt\")\n",
    "ax.set(title=\"Monthly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 天气和骑车数目的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'weathersit distribution of counts')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['weathersit',\n",
    "                       'cnt']],\n",
    "           x=\"weathersit\",y=\"cnt\")\n",
    "ax.set(title=\"weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 工作日和节假日的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0ca50e160>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(ncols=2)\n",
    "sn.barplot(data=train,x='holiday',y='cnt',hue='season',ax=ax1)\n",
    "sn.barplot(data=train,x='workingday',y='cnt',hue='season',ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0ca480e48>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6J0UCi6crdpt3B/H7wKn8r/1oatzfjBKusc9eYM2o2RxZvMXtMZq93JdzWw+bjpkevbt3Yta0dzPl2EKkxh5nN/XIrXJ9C0MpNQ4YAeQHlgAjtNbXlVLzgHJa645OZfsDC7TWFsfyBKA/8DowESgLrAIGAh2B94AywErgSa31FU/VO6heVS6fDOfK6fMA6N+3UrVzQy4ePZdQJupMJJD87FNbbOL1brzz+2DxsqQrdmC9qlw5GU6UI/bRJVup0rkhO51iX42PbU/+x+FfpxKF/H05vT6EgHsqpyu2GY3q1eFsaLjHjytEmqSFkav1BUoCbYHHgN7A6HQeIxgYBPQBugEtgJ+AoUA/oDvQCiOpeEzRoBJcPXcxYfla6EWKBpYwv39wSfqvnMTQfz5ix+d/cD38sul9i7iJXSTIZGyLhZbjHmfLu9+ZjifEHcNm8pFL5fYWxmmt9YuO54eVUt8DnYHx6ThGAWCQ1joSQCm1CBgOBGmtzzvWfQ908Fy1AUvyVoGbL/MpuhZ6kW+7vE6RwOL0mvMiR5dt40Zk1G3HxmTsOgM7cnLtHq6FXky7sBB3mNw8oG1Gbk8Ye1yWz2IkjPQ4G58sHMKAsPhk4bQu4Dbql6JroRcpVqZkwnLR4JJcj7iU7uNcD7/MhSNnKdtEJQyKp7mPu9jh5mIHNaxGmSaKOgM74lOkIN4++Yi9fpO/J/+Q7roLkePk4taDGbk9YdxyWbaT2A1nA1y/Svu4OUasm2O4W+fR7r2wvf9SonIQvuX9uRZ2EdWzGcuf/8zUvkWDShJ96SrWm7EU8CtMmUbV2TlnmenY4Xv/xa9SEMXK+3M97CLVezVj1XPmYq9+/vOE53c91IqAeypLshC5Rla0MJRS3YFJQE0gFPhYaz0tHftPwOhF+UprPdSTdcvtCSM1EcC9LusaZEdF3LFbbawdN58HF4zG4u3FgR82cOHIWe4d1YfwfSf4d/UuAu+pQs85IynoV5gqHetz76g+fNPxVUpWL0PrsY8ZfVgWCzu/WMYFfSZdsTeOm8/93xqxD/6wgYtHztLkpT5EhJzg5OpdBNStQvc5IyngV5jKHevTZFQfvuv4aia+I4leGT+Z7btDuHw5ig69+/PMkwPo07NLlsQWeZs9LnOPr5RqBPwGfAg8CjQFZimlbmit05xDrpRqjzHmGpIZ9cvLCeNPYIxS6llgOdAeYxA7xzi5bi/z1u1Nsu7vaT8nPA8P+Zcvmz6fbL/Tm/bzbZeMjcGfWreXUy6xt32YGDti77/Ma5I8trPDP27i8I+bMlQPd95/K2sSkxDJmOySUkqlOctEa+1urvsoYLvWOv6X/JBSqjYwBkg1YSilAoFvgEeATJl3nttnSaVIa/0nMBZ4DdiLkTDeztZKCSFyNLvN3CMDWgArXNatACoppcqltJNSygtYCMzWWv+VoRqkIte2MLTWbd2sexenzKu1nohxfoWzmU7bJwATUjuGY91kYHIGqyyEyOlMJoMUWg9mBGNMonEW5rQtpb7lcRif566fZx6VaxOGEEJ4WgZbDxkO726lUqo18AzQQGudqTWUhCGEECZlQcIIBYJc1gU6frq2POK1B/yBU0qp+HXeQGul1GCgotb6rCcql2fHMIQQIr3sVoupRwZsBlyn/HUFTmmd4lTHz4B7gHpOjx3Ar47nHruOjrQwhBDCpCxoYUwHtiilJgILgCbAc0D8FStQSjXBmA01UGu9TWsdgXGaAE5lrgOXtNb7PVk5aWEIIYRJdpvF1ON2aa23Y1zz7j6M2ZvvAG+4nINRGFCOn1lKWhhCCGFSVgx6a62XAktT2b6e5FepcC3T1rO1MkjCEEIIk+z2DI1P3PEkYQghhEm2OEkYIpt9cuNAtsW+EReTbbFHlmmVbbGjz20iNvLfbIvvU7pKtsUWty89txjIjSRhCCGESRkZ0M4NJGEIIYRJkjCEEEKYIl1SQgghTJEWhhBCCFNsGbvsxx1PEoYQQphkk/MwhBBCmCEn7gkhhDBFxjCEEEKYIrOkhBBCmJLXWxgev7y5UuqkUmqsB44zQSl1zBN1ygpKKbtSqn9210MIkXmsNi9Tj9wqM15ZY4ybgAgPeHPSaNZu+41lG36g9j13uS3z0usj+Gvvcvad3JxsW/f7O7Fy88+s+OsnZsyelO747055nb93rWDt5sXUqVvLbZlXx77Azv1rOX5mR5L1ZcsF8/Pv81i98WfWbl5Mh06t0xV7+rS3OXzwL3btXE39encn216oUEGWLP6G/fs2sHfPWiZNfC1h28AB/Qg9G8KO7avYsX0VQ554NF2xUzJ20jRa93iE3v2He+R44s5it5t75FYe75LSWp/39DHzqrYdW1KpSgXaN7mfeg3r8M77r/Ngl4HJyq1ZuZFvvvqBtf/8lmR9pSoVePqFITzUfTBRV65SqnSJdMXv0Kk1VapU5N4GXWnQqC5TPnyT7h0fSVZu1Yr1fD3nf/y9c3mS9SNfHs6SX1cw/+vvqaGqsvDH2TS+p6Op2N26tqd6tcrcVaslTZs0YOan79G8Zc9k5aZNn8X6DVvw8fFh9cof6NqlHStWrgNg0Y9LeGFkhhu7SfTu3onH+vTi9Xc+8OhxxZ1BptWmQSnVEeNmHiW01jeUUgWBy8AOrXVLR5l2wGqgJBACfKm1ftex7STG7QT9gAFALMatB8dora2OMgWAGcBjgA343hHDuR61gWlAU8AHOA1M0lovcGy3AyOB1hj3wL0CfKC1nuZ0jKLARKAvUALQwDta61+cygQCU4AeQEHH63lNa73RqUw74COgBnAEeCGt9/F2dOzWhl8X/QHAnp378PUrhn9gac6HRyYpt2fnPrf7PzzgARZ8vYioK1cBuBB5KV3xu3Rvz6LvjSS0a8defP18CQj0JyI86XeCXTv2ut3fbrdTrFhRAIr5FiMsNMJtOXd69uzCgoU/AfDPtl34FfcjKCiAsLDEY0RHx7B+wxYAYmNj2bV7H2XLBpt/gbehUb06nA312C2SxR0mr0+rNdMltRmwA/HXom4BXAWaOD6AAdpjJJCoFI7xHBCK8WH/PMYHu/NX5clAH8e6e4HrwAiXY3wHXACaA3WAUYDrJ+B4YD1QH+NDf6pS6kEApZQF+B2oCzwM3A18DnyvlOrgKFMIWAcUA7o5jrMMWK2UqukoUwb4A9gJNABewkgeHhcUHEDo2bCE5bBz4QQFB5jev3LVilSuWoFFS+fy84r5tG7fPF3xg4MDOecUP/RcGMHpiP/B5Jn06deTXQfWsfDHWbwx+l3T+5YtE8SZ/84lLJ89E0rZMkEplvfz8+W+Hp1Yu+6vhHUPPtCdXTtX88P3X1CuXBnTsYVIiXRJpUFrHa2U2gp0AFZiJIclGB/srTE+UNsDa1M5zCat9WTH86NKqSeAzsBcpVQR4GngOa11fJ/Ky0qptkBxp2NUBKZprQ86lt3dzGCp1voTx/MjSqmmGInlF6CNo86BWusrjjJfKKWaYSS0NRiJxBd4WGsd5ygz0ZFQhmEkumeASOApR5mDSqnXMZKRR1ksyb/N2NPx25gvnzeVqlTgsfufIqhMAD/88TVdW/blatS1LIn/QN/u/PDdr8z6dB4NG9fj09lTaHNvL1PHSE9sb29vFi6Yyaczv+bEidMA/LF0Nd//sJhbt27xf08NYO5XM+jUpZ/pugvhTm4e0DbD7BjGWqCX43l74BMgBmivlNqIMdA9PpX997gsnwUqO55XBQoAW1zK/IVxI/R4HwBfKqUGY7Qilmitd7ns87fL8maM7ikcdcwPnFVKOZfJDxx1KhMEXHYpUwCIdjyvBWxzSijxdfWIAUP68fCABwEI2XOA4LKJ36qDygQSHmZ+iCjsXAS7d4QQFxfHmdPnOHHsJJWrViBk98EU93li6GM8PqgvAHt27aeMU/zgMkGEpSP+Y/378mjfpwDYuX0PBQoWoFSpEkRGXnRb/unhg3jyyccB2LFjD+XKJ7YKypYL5lwKXUGzPp/K0WMn+PiTLxPWXbyY2Pj88quFvDfpddP1FiIleX0Mw2y6XAvUV0pVABo6ltditDpaYYw7JJ+ik+iWy7LdKbbFaV2KtNbvYIwZLMLoTtqqlEqrj8P5f9cLY1yjnsujFkb3U3yZQ27K1ASecjqma1091ghd8PUi7mv3CPe1e4TVy9bxQD8jZ9ZrWIerUdeSjV+kZtWydTRr2RiAEiWLU6lqRU6fPJvqPnO//B8dWz1Ix1YPsmLpGvo9cj8ADRrV5WrU1WTjF6k5e+Ycrdo0A6B6jSoUKFAgxWQB8Pms+TRq3JlGjTuzZMlKBjxuJK6mTRoQdSUqyfhFvLffGo2fXzFGvZT0+0pQUGLXWc+enTl8+I6ZoS1yMLvJR25ltoXxD8Y37DeBo1rrMKXUOuAH4CFgq9Y6OrUDpOIYRkJpATh/9U3W4a61/hf4DPhMKfUq8ArgPA2mmWN7vHsxEgDADowuroJa6/0p1GUHxjhKlNY6pRHaA8AApZR3/KA90DKFshmybvVftO3YknXblxATHcPo5yckbPtj3ffc186YsTRm/Av06tONQoULsjlkBYu+/ZWPps5m49ottGp3Lys3/4zNamXyhBlcvnQlhWjJ/blqAx06tWbr7pVE34hh5IjEb+l/bvqFjq2MltC4t17mgb49KFS4ELsOrON/C37ig8kzmTB2Kh989Db/98wg7HY7LzzzWkqhklm2fA1du7ZHH9rMjehohg4dlbBtx/ZVNGrcmbJlg3n9tRc4dPgo27etBOCzz+by9dzveO7ZIdx3X2fi4qxcuniZIUNHmo6dmlfGT2b77hAuX46iQ+/+PPPkAPr07OKRY4ucL6+3MCxm+6SVUiswWhSztNbPOdbtxvi2/47W+m3HupMknyWVsOxY9yVQTWvd1rH8EfAIxrd4DTwJDAcitNbVHIPrU4CfgRMYH/zTAW+tdSvHMewYg+BvYoy1dHWUeURr/ZNj0HsVxljIGGAvxkyp5kCM1nqOYwbYDozk+AbGDKhAjG64Q1rrxUqpshhdWN9hdJOVAT7EGEwfoLX+1tQb6qRK6frZ9qUkO+/pHXkjpTkSmS/63KZsiw1yT+9skuFP+81BfU39rbYI+ylXZpb0jOCswWiROA9ur3Wz7na8CizGmG67DSMhzHTaHofx4f4VRothJRCOMQ3X2dtAR4xk8DrGdNifALTWdoxxmF8wpucexpgu3AM47igTgzE4vgOYi5EwfgGaAKccZc4CPR3r9mDMkEr8+iuEyLVsJh+5lekWRk7naGHc1jf87CYtjKwnLYw8KcPf+tcHPmTqb7Vt+I+5soUhFx8UQgiTbBnPOXc0SRhCCGGSXRJG7qC1ztv/k0KITJebxyfMyDUJQwghMpu0MIQQQpgSl3aRXE0ShhBCmJQVLQylVHdgEsYVJkKBj52vuu2mfHFgAtAJqIRxcdjNwBta68OerFvevpKWEEKkg81i7nG7lFKNgN+AFRiXJZoATFJKpXbHrmCMa/O9iXEF7fuAosBapVT6boKTBmlhCCGESVkwrXYUsF1r/apj+ZDjXkBjgFnudtBaHwLud16nlHoM46raLfHglbQlYeQA+8fUz+4qZIuuM05lW+yqNe5Pu1AmOX7kN2Ij3V2dP2vISYO3z+wZtkqpy2mV0VoXd7O6BcYVLZytwLjlQzmt9RmTVfBz/DR/tVITpEtKCCFMyoJLgwQDYS7rwpy2pUkp5Y1xEdbtGBeO9RhpYQghhElWNzf2cieF1kNGpdnAcSSLbzBuBdFaa+3RU0ckYQghhElZcOJeKMZN3JwFOn66tjySUErlx7iKdl2gbTq6r0yTLikhhDAps2dJYUyHdb3BSlfgVGoJQClVGOPW2bUwWhanM1SLFEgLQwghTMqCWVLTgS1KqYkYt3toAjwHvBhfQCnVBKPbaaDWeptSqhiwDCiHMVvKppSKb6VcycDN7ZKRFoYQQpiU2bdo1VpvB3pjnEuxF3gH4wQ85ym1hQHl+AnGbbNbYpy0txejWyv+8XAGqpOMtDCEEMKkDHY3maK1Xopxc7eUtq/H6d4ersuZSRKGEEKYZM3uCmQzSRhCCGFSVrQwcjJJGEIIYVJevx+GDHp7gFJqglLqWHbXQwiRubLBQI9zAAAgAElEQVTgTO8cTVoYOZhXpbvJ3+ExsFiIC9lE3LZlSbZ7125B/rb9sF+7BEDsrjVY921KLJC/IAWHTMR6dBexaxbm+NjPvz2CZu2bcjP6Ju+9OJUj+48mK1OjTnVenz6a/AULsHXtP3z85kwAJnw+lvJVywNQ1Lco16Ku8WTnYXR6oAOPPN0vYf+qNaswtOtwTu6ISLUub733Ku06tSI6OoaXRoxlf8ihZGVeeeM5+jzSCz8/X2pWaJqw/s2Jo7m3ZWMAChUqSCn/ktSp3MLUe5CasZOmsXHzNkqWKM7ib91eh05kMrt0SYkcyWIhf6f+3Fz0IfarFyk44E2sx/dgv3AuSbG4w9tS/ED2afkAtv/0HRG7WfsmlKtcjsdaDqRWg5qMeu8Fhvd8Nlm5l94byftjpnNg50GmLniPpu2a8M+6bUx4+t2EMiPeHM61qOsArP51Dat/XQNAlbsqM+nrtzl24HiqdWnXsRWVqlakdaMe1G90DxM/HMv9nR5PVu7PlRuY/+V3bNiedELL229MTXg++KnHqH3PXabfh9T07t6Jx/r04vV3PvDI8UT6yQ2U7gBKqRHACKAqcAXYqLXu67iE7wvAXUAsxoW2XtRaH3Ha93VgKFAWiAJ2Ab211tFKqQlAf611NafyLYFNQGWt9UnH9eQ/AVoD/sBp4AtgmtY6I1OuU+UVXAX7pQjsV84DEHf4H7yr1SPO5UM7JZbAilgK+2I9sR+voEo5PnbLLi1Y+dMqAA7uOkRRv6KUCijJhYiLCWVKBZSkcLHCHNh5EICVP62iVdcW/LNuW5JjtevZhpH9Xk4Wo0Pv9vz527o069K5ezt+/n4JALt3hODrW4yAwNJEhCe98OfuHSFpHqtXn25Mn/xZmuXMaFSvDmdDwz1yLHF7Mu0P/g6R48cwlFJvAVMwrr5YB+M0+T2OzQUwTmxpgHG3KSuw1HFNFZRSDwKvYiSV6o4yy9NZhQLAPoyTaWo54r0FDL7d12SGpWhx7FcTPyztVy9hKZr8Xij5ajSk4OC3yN/rGSzF4rdbyN/2YWI3LLpjYpcOKk3EufMJy+dDz1M6qHSyMudDnctEJitTt2kdLp6/xJkTZ5PFaN+zLWsWr02zLkHBAYSeTbxsT9i5cIKCA0y/lnhlywVToUJZNm/06AVDRTbKgkuD5Gg5uoWhlCoCjAbGaa0/ddq0C0BrPdel/GDgAtAY45osFTEu2LVCax2L0TrYQzporcMwEla8E0qpxsBjwFz3e3mCu9+6pN9vrMf3EH34H7DGka9uW/J3G8rNRe+Tr347rCdCsF+9dMfEdncRULvd7lImeSHXMh16t2eNm1ZEzfp3cTM6hhP65G1Vxn4bXy17PdiNpUtWY7Pl5mHQvCWv/0/m6IQB1AYKAqvcbVRK1QPGY9zKsDSJn3QVMRLGIuB54JRSahWwBlistb5qtgJKKS+MpPUIxrVaCgI+QKbe/cd+7RKWYiUTli3FSmC/5nJPlpjrCU/jQjbg06YvAF5lquJVrgb56rXH4lMAvPNB7E1iN/6Uo2I/MOh+7nu8OwCH92gCyvgnbPMP9udC+IUk5c+Hnsc/2LlM6SRlvL29aN2tFU91S343yw73t0u1O2rgk4/w6MA+AITs3k9w2cQLhgaVCSQ8LPVBcnd6PtiVcaMnpns/kXNJwrgzJPt+57g64yrgL2AIiZf+PQDkB9Ban1VK3QW0A9oD44ApSqmmWuv/MP7/Xb9O+rgsvwS8hnHrxF0YN1h/EeiR8ZeVMlvoCSwlArH4lcZ+9RL57mrKzT9mJy1UxA+uXwHAu1p9bBdCAbi1dE5CEe/aLfAKqmQ6WWRl7F/n/8av838DoFmHpjw4uDdrfltHrQY1uR51Pcn4BcCFiIvcuHaDWg1qcnDXIbr07cwvc39N2N6wVUNOHzvN+dCkYw0Wi4W297XhuQdfJCXffPU933z1PQDtO7Vi0FOPseSX5dRvdA9Xo64lG79IS5VqlfAr7svObXvTtZ/I2fL6GEZOTxgHgRiMy/3uc9lWE2MQ+g3HPW1RSjXHJQForW9i3OJwhVJqHBCOMR7xCRABBCilvLXW8Wf9N3CJ0xqjSyvhtolKqeoeeG2ps9u49ee3FOg7Cry8iNv3F/YL5/Bp0Rtb2Emsx/fg06Aj3tXqgc2GPeYat5a73tnxzom9dc0/3Nu+Kd9tXsDN6BjeG/V+wravVs3myc7DAJj22ke8Nn00BQoW4J9129i6NnHA22hFJB+jqNvsHs6Hnif0dKipuqxdvYl2nVqzaecyoqNjePnZsQnblm/4kW5tHgLg9Qkvcn/fHhQqXJB/9v/J9wt+ZvqUzwG4v083fv9lRfrfiFS8Mn4y23eHcPlyFB169+eZJwfQp6frlbBFZorLxeMTZlhc+4BzGqXUu8BIjJugrwYKAd2BOcAZ4GvgQ4wrNU7GuHLjEK31PKXUkxgD+9uAy0AHx36dtdZrlFIKIylNwbiPbgPH88okzpL6ABiA0SV1FhiIcbnhS1rrSo46TsBltlV63Hh/SM7+T8gk2XlP75PR6e9i8pTjR37LttiQp+/pneGP+/cq9jf1t/raqW9zZWrJ8bOkMLqR3sAYi9iP0Q3VQGsdCfTHmPl0APgAeJmk3YyXgCeA9cAhjG6l/9NarwHQWmvgKYxksB+ja+t1l/jvABuA34C/gRLAxx5+jUKIO4ANu6lHbpXjWxh5gbQwsp60MPKkDH/rf6fi46b+VsedWpgrWxg5fQxDCCFyjDz5zc6JJAwhhDBJptUKIYQwJc6St9sYkjCEEMKkvJ0uJGEIIYRp0iUlhBDClNw8ZdYMSRhCCGFS3k4XkjCEEMK0uDyeMiRh5AS2bPwl9Mq+84tis7FH+GLMtWyLfeXxJ7Ittt/CucRG/ptt8e/0kwbzdrqQhCGEEKbJoLcQQghT7Hm8jSEJQwghTJIWhhBCCFNkWq0QQghTrJIwhBBCmCFdUkIIIUyRQW8hhBCmZEULQynVHZgE1ARCgY+11tNM7DcaGAEEYtxhdIzWepUn63Yn3KJVCCFyBLvJf7dLKdUI43bQK4B6wARgklJqeBr7jQTewrildX1gNfC7Uuqe266MG9LCEEIIk8y2MJRSl9Mqo7Uu7mb1KGC71vpVx/IhpVRtYAwwK4VYFuAVYLrW+hvH6tFKqXaO4w02We00SQtDCCFMstrtph4Z0AKjdeFsBVBJKVUuhX0qAWVS2K9lRirjKke1MJRS64FjWuuh2V2X9FBKzQPKaa07evK4XpXvJn+Hx8DLi7i9G4n7Z1mS7d53tyB/u4exX70EQOyuNVhDNiYWyF+QgkMnYT2yi9g/v01f7EqO2BYLcSGbiNvmErt2C/K37Yf9mlPsfZuSxh4yEevRXcSuWWgq5otvP0fz9k2JiY7hnRencGT/0WRlVJ0ajJs+hgIFC7Bl7T9Mf/MTAKrVqsroyS9SuHAhQs+EMf7Zidy4dgPfEr5M+mICNevexbJFK/hw7Mem6vL+B+Pp3KUt0TdiGDbsZfbuOZBke6FCBVmwcCZVKlfEarWybNkaxr85FYBnn3uSwYMfJi7OSmTkBZ4ePob//jtrKq4zn0ZNKDL8OSzeXsQsX0r0ov+5LZe/ZRt8x73N5Wf/j7ijOt1xzBg7aRobN2+jZIniLP7W7RfdPMHseRgptB7MCAbCXNaFOW07k8I+zuWc9wvGg3JaC+NBjCZUmpRS5ZRSdqVU28ytUjaxWMjfaQA3f5xOzJdvkK9WUyylyiQrFndoGzHzxhMzb3zSZAH4tHoQ23+38QFisZC/U39u/jSdmK/Hkq9mCrEPbyNm/gRi5k9ImiwAn5YPpCv2ve2bUr5yWR5q2Z/JYz5k9Hsvui03+r2RTB7zIQ+17E/5ymVp1q4JAK+9/zKfT5pD/45PsmH5X/R/+mEAbsXc4oupX/PpO5+brkvnLm2pWq0Sdeu047lnX2PGR++6LffxjDk0qN+R5vfex733NqJT5zYAhOw9QKuWvWjWtBuLFy/n3Ymvut0/VV5eFB0xkqixo7n01CAKtOuAd4WKyYpZChWiUO8+xB464OYgntO7eydmTXP/PuQlmT2GkWb4rNknRRlOGEqp/J6oCIDW+qLWOspTxzNLKeXj6AfMMbyCq2C/HIH9ynmwWYk7tA3v6vVN728JrIiliC/WE/tvL/Ylp9iH/8G7Wr30xS7si/Wk+Q+x1l1asPwnY0LHgV2HKOpXhFIBJZOUKRVQkiLFirB/50EAlv+0ijZdjRZ3xarl2b11LwDbNu2gbffWAMRExxCyfT83b94yXZf77uvEdwt/AWD79j34+fkSGOSfpEx0dAwbN24FIDY2lj179lO2rPFlbuPGrURHxxh12babMmWDTMeOl0/VxHruLLawUIiL4+b6teS/N3nvQuFBT3Ljx+/glvnXdzsa1auDn2+xTI1xJ7CZfGRAKOD6CxPo+OnagnDehxT2S2mf25LuLilHt9Fx4BwwFMinlAoGxgKDMJpAxzGmgs122q8yMBtoDUQAU4CHcOqCcu2SUkq1dJSLH+n/FxittV4J/OdYt04pBXBKa13JsV8njNkFDYCLwCrgZa31Bcf2eUA5jNkILwEVAF/gmlLqOYypaZUcMeYBU7TWcY59S2AMPt0HXAPmAB5PNpZiJbBHXUxYtl+9iFdw1WTl8qmGeJevge1SGLFrvsd+9SJgIX/7R7j1xxy8KtZMf+yixR3HiY99Ca/g5JelzlfDEftiOLHrvnN0jVnI3/Zhbi2bg1eFWqZj+geVJvxcRMLy+dBI/INKcyHiYpIyEaHnE5YjQs/jH1QagH/1CVp1bsGmVZtpf19bAsoEpOclJxFcJpAzZ0ITls+dDaVMmSDCw867Le/nV4xu3Tvw2cy5ybYNGvQwq1dtSHcdvEqVxnY+8f2wRZ4n311J/y+9q1bHyz+A2H/+hj4PpzuGSL8suDTIZqAL8LbTuq4Yn2/uuqMATmJ8HncBnLsZugJ/ebJytzuG0Q9YCHQAvIEvMT6chwFHgSbAbKVUnNb6K8e391+BmxgJ4xbGPOP6wDF3AZRS3sASjA/swY7VdwM3HM8bALuAPsAWwOrYrz1GIhjj2K84MBX4VSnVRmsd/z/eBLgK9HbsG6OUmgA8AYwE9mDMg54FFMSYrgbwNVAH6AmEA68BvYBtJt+7DEj6y2o9tofoQ/+ANY589dqSv8dQbn4/lXwN2mM9HpLkQz993OU/l9jH9xB92BG7blvydxvKzUXvk69+O6wnQhLGVcyHTB7TdezQ4raMUWjiqKm8+M5zDHlxIJtWbSYuNjZ98U3GceXt7c3c+R/z+WfzOHnyvyTbHn6kN/Ub1KFr50dupxLJ19mTbi86bARXP5yc/mOL25YFlwaZDmxRSk0EFmB8Tj0HJPTRKqWaAN8AA7XW27TWdqXU+xjTbw8BOzA+++oCT3mycrebMEKBZ7TWNkfLYSBQS2t92LH9hDK+9j8HfAV0xKh8da31MQClVH/cD+DE8wVKAEu01vGjn86joPFf9y5qrZ2bXW9itG4+iV+hlBoEnHLUYY9jtQ0YoLW+5ihTGBgNPKi1jp9tcEIpNRb4GBinlKqGkWA6a63XOvYbApxI5XXcFvvVS1h8E7tkLMVKYr/mMlMv5nrC07i9G/Bp+xAAXmWq4lW+BvkatMfiUwC880FsDLEbfjIX+9olLMWcY5dIPXbIBnza9E2MXa4G+eo5x75J7MbksfsM6k2vx3sAcGjPYQKdWgX+waWJDI9MUj4i9DwBwYldQwHB/kSGXwDg1PH/GPnYaADKVylHiw7NTL3WeP83bACDnzA+2HfuDKFcucSxwjJlgwkNDXe73yczJ3H82MlkrYu27VowevQIunZ5hFu30V1kizyPl3/i++FV2h/bhcT3w1KoMN6VKuM3dYaxvWRJir01iavjX8+0gW+R8hcHT9Fab1dK9cb4Qv0yRpfSG1pr55kGhQHl+Bm/3wzH8MAkEk/c66W13uvJ+t1uwtiptY7vqmuE8ZV0h6NryPnYVsfzWkBkfLIAY7xCKZXib7bW+pJS6ktgpVJqLbAB+FVrndZfQ2OgmVLqWTfbqpOYMA7FJwuH2kAh4GellPNvhTdQUCnl73gdYLRo4ut5Sym1HSiaRr3SxRZ6AkuJACx+pbFfvUS+mk24+fvspIWK+MH1K0Ylq9XHdsHoRrn1xxeJlb+7BV5BlU0ni8TYgYmx72rKzT9Mxl46JzF27RZ4BVVymywAfp6/mJ/nLwageYdm9B3cm9W/raV2g5pcj7qepDsK4ELERa5fu0HtBjU5sOsQ3fp25se5vwJQolRxLl24jMVi4YkXBvDrgt9Nv16AL2Yv4IvZCwDo0rUdw4YP5Mcff6dx43pERV112x315viX8PMtxoinkw5q31O3Fh9/MpEH7h/M+fMX0lWPeHH6MN5ly+EVGITtQiQF2rbn6uR3Erbbb1znYr/7E5b9ps7g+pzPJVlksqy4Wq3WeimwNJXt63HTDaC1norRm5JpbjdhXHd6Hj9w3pzE7qJ49hSem6K1fkop9RHQGegEvKOUetZ5bMQNL4xxjwVutjm3RK67bIt/HQ8BR9zsawwOZBW7jVurF1Kg30tg8SJu3ybskefwadkbW9hJrMf24NOwE97V64HNij36OreWfum52H9+S4G+o4wpvfv+wn7hHD4tHLGP78GnQUdjINxmwx5zjVvLv8pQyC1rttK8fVN+3PwtN6Nv8u6oKQnb5q+aw6DORsv6/demM3b6qxQomJ+t67bx99p/AOjUuwN9BhsfoOuXbeKPH5Yn7P/L1u8oUrQw+fL70LprS1549BX2bTuZYl1WrlhHly7tCNm/nugb0QwfPjqxnluX0rxZD8qUDWL0mGfRh4+x+e8/AJg96xvmz/uBiRNfo2iRIixYOBOA//47x8MPpbNnwGbl2swZ+E36ALy8iFm1DOupkxQeOIS4I4e5tXVL2sfwoFfGT2b77hAuX46iQ+/+PPPkAPr07JKldcgJ8vrFBy3pbWK5GZiuhtFV1FNr/UcK+3TCGHh27pIqgdEl9V1Kg95ujjMLaKy1bqiUCsAYQ+iotV7jVGYTEK617pvKa5iHy3kTSqmiGN1cr2itP01hv/jX2llrvdqxLj9Gl9Sh2z0P48aUJ7LvimbZeE/vDjM83pNn2r5LJ7Mt9slWFbIttt/C5APzWSmb7+md4V/2+yr0MPW3+sfppTlq1qWnZPjEPa31MaXU18Acx8Wv/gaKAA0Bf631FOBPYC/wjVLqBYxB74lAHCm0PBwfzk8Bv2PMVioDtMIY6AaIxJil1FkpdQC4qbW+hDGGsUopNR2YjzGwXR2j5fCs1jo6hddxTSk1CWPgCIxrseTDGOCur7Ue43itS4CZSqlhGAnrVUDmGwqRB+T1Gyh56sS9/8MY3X8DOAiswZhi+y+AY2bSAxjdQJuAP4DlgAZiUjjmdYwP+u8xuoh+xhg7eNZxTBvG9Nd+GAllt2P9OqA9xgf9JiDEUberQKpTZ7TW72DMRhiKkeD+ciyfdCo2BGMc5A+McZWzGDPAhBC5XBZcGiRHS3eXlKcopYphdEmNdZ7RlBdJl1TWky6p7HGnd0l1Lt/V1N/qqv9WSJdURiilemF0QR0CAoDxGN1Ri7KqDkIIkRF5vUsqKy8+WBhjfKESRnfTTqCl1tr9BHchhMhhsqtHJqfIsoShtf4eYzxCCCHuSNLCEEIIYYrVnrfPxJCEIYQQJuXt9oUkDCGEME26pIQQQpgiCUNkO0vV5Pe5yDJe2XfTRav9eLbFjrXFZVvsIqMfz7bY2S028t9si+2Jc0BklpQQQghTpIUhhBDCFJvMkhJCCGGGtDCEEEKYImMYQgghTJEWhhBCCFPskjCEEEKYYZMuKSGEEGbItaSEEEKYIl1SQgghTJEuKSGEEKZIC0PkWJuPnGXq0h3YbHYeaFSNIW3uTlZm5b6TzF4TAhaoEVSCyQ+34tyla7z0vw1YbXbibDYebXYXDzWtkb7Y+ixT/9hmxG5cnSFt6ySPHXKS2Wv2AFAjuCSTH2ltxP52HVa7nTirjUeb1+Shpur23gAnL73zPM3bNyUm+iZvv/geet/RZGWeHjOU7g91oZhfUdpW75aheNM+fIuuXdtz40Y0Q58axZ49+5NsL1SoIN/9bxZVqlTEarWydOmfjB03GYCnhvZn+PBBWK1Wrl2/zjPPvMrhw8nr687mAyeY+uM6bHY7DzS/myFdmiYrs3KnZvbSLWCxUKOsP5OH9GC7Ps37P69PKHMy7CKTh/Sgfb3qpl/zX1t3MHnGLKw2G316dmXogH5Jtp8LC2fcpOlcvHwFP99iTH7zFYIC/AG4p1UPqlepBEBwoD+fTp1gOm5axk6axsbN2yhZojiLv53lsePeDmlhiAxRSv0JnNFaD/bkca02G+/9vo1ZT3Qk0Lcwj3++nDY1y1E1oHhCmVORUXy9YT/zhnXBt1ABLl6LBsC/WCHmD+tK/nze3LgZS5+Pf6dNzXIE+BY2H3vJVmY92dmIPXMpbWqWp2qgS+z1+5g3vFvy2E93T4w94zfa1CxvOrY7zds3pXzlcvRp8Th3N6jFmPdGMeS+p5OV27R6C4vm/sLPmxfediyArl3aUa1aZWrVbkWTJvX55ONJtGrdK1m56TNms2HD3/j4+LBixfd06dyWlavW8/0Pi5nz5bcA3NejE+9PfZOevQakGddqs/HeD2uY9XxfAosX4/EpC2lzTzWqBpdKKHMq4hJfr/yHeS8/im/hgly8egOAxqoCi14fCMCV69H0HP8199aqZPo1W61W3v1wJnNmTCIooDQPD32Bdi2bUrVyxYQyH3z6Jb26duD+7p34Z+ceZsyax+Q3XwGgQIH8/Dx/pul46dG7eyce69OL19/5IFOOnx42uzW7q5Ctsu9SpSJV+89coHzJYpQrWQyffN50uaci6w/9l6TMLzuO8nBThW+hAgCULFoIAJ983uTP5w3ALast3Wen7v8vkvKlfBNj162cPPb2Izx8bxqx46x44gtZ6y4tWfbTSqNuuw5SzK8opQJKJq/3roNciLiY4Xg9e3bm24U/A7Bt226KF/clKCggSZno6Bg2bPgbgNjYWPbs3kfZcsEAXL16LaFc4SKFTb//+0+GUd6/OOVKFzfe94aK9XuPJSnzy18hPNymHr6FCwJQsljyRLx691Fa1K5Eofw+Jl8x7Dt0hArlylC+bDA+Pj5069CGtZu2Jilz/MRpmjaqB0CTBnVZt+lv08fPiEb16uDnWyxLYqXFht3UI7eSFgaglBoBjACqAleAjVrrvkqpk8A3gB8wAIgFFgBjtNZWpdQ8oIPjGIMch2untV6f0TpFRN0gyK9IwnKgbxH2/ReZpMypyCgABs1egc1uZ3j7e2hRoywAYZev89w3a/nv4lVGdm2Yrm/4yWMXZt9/593HnrUMm83O8A71aKGcYs9fw38XohjZrVGGWhcAAUGlCT8XkVi/c+cJCPL3SHJwp0yZIM6cOZewfPZsKGXKBBEWFuG2vJ+fLz16dOTTmV8nrBs+bBAvvPAUPvl96NrlYVNxIy5fI6hE4gdjYIli7DsZmqTMqYhLAAz64Dvjfe9xLy1qV05SZuWOwwzo0NBUzITY5yMTupcAAgNKs++ATlJGVa/C6vWbGdCvN39u2ML1G9FcvhJFcT9fbt26Rb8hz5PP24snB/SjQ+vm6Yp/p8jrlwbJ8y0MpdRbwBTgM6AO0BXY41TkOSAUaAo8D4wEBjq2vQBsAhYBwY7HFk/Uy93vpcWSdNlqs3M68ipfDu3M5H4teevXrURF3wIgqHgRfny+J0tG9eb3Xce54OgyMhXbzTqLS3Cr1c7pyCi+fKorkx9pzVu/bEka+4VeLHn5QX7fdYwLV83Hdsv1hZO5f7iurzW1eN7e3iz45lNmzpzLiROnE9bPmj2fmrVa8sYb7/Hqa8+biutuQNW1JlabndPnL/Pli/2YPKQHby1cRdSNmITt569c49i5yHR1R4G537eXRwxlx+599B08gh179hHoXwpvb6M1ufrnb1j09cdMmTCGKR/N5rRTws1NpIWRhymligCjgXFa60+dNu1yer5Jaz3Z8fyoUuoJoDMwV2t9RSl1C4jWWod5sm6BfoUJu3I9YTk86jr+voWSlvEtTJ0K/vh4e1G2ZDEqlfbl9IUo7i5XOqFMgG9hqgYWZ9fJCDrdXREzAn1dY9/A36WVEOjnEtvfl9ORUdxd3l3scDrVqZSel0/fwb3p/fh9ABzcowksk9glFFDGn/PhkSnteluGDxvEkCGPArBj517KlSuTsK1s2WBCQ8Pd7vfZZ1M4duwEn3z6ldvtixb9xicfTzRVh8DixQi7dDVhOfzSVfz9irqUKUqdysH4eHtTtrQflQJLcjriMndXCgJg1c4jtKtbDR/HB7lZgQGlCYtIbEWGR0TiX7pUkjIB/qX46L1xANy4Ec2f6/+iWNEiCdsAypcNpnH9ezh89DgVnN7D3EJaGHlbbaAgsCqVMntcls8CgZlWI4faZUtx+sJVzl68SmyclZUhp2hzV/kkZdrVKs/2f408del6DKcuRFGuZDHCr1wnJta4o1xU9E32nIqgUmlf87HLleZ0ZFRi7L0naFOznEvsCmw/7hQ7MopyJYsmj33yPJX8/dL9+n+at5j+nYbSv9NQNqzYRPe+XQC4u0EtrkVd93h31KzZ82nStCtNmnbl9yUr6f94HwCaNKnPlStX3XZHTZjwCn6+xXjp5QlJ1lerWinhefduHTh27KSpOtSuGMTpiMucjbxivO87NW3uSXo3xnZ1q7H9iDGedOnaDU6FX6Rc6cT3d8WOw3RrdJepeM7uvqsGp8+c48y5MGJjY1m+ZgPtWjZLUubS5SvYbMaZznMW/MADPToDcCXqKrdu3Uoos3vfQapWqpDuOtwJbHa7qUdWUUp1V/kd8l8AABelSURBVErtUUrdVEqdVEqNSqN8caXUDKXUAaXUdaVUmFLqZ6WUqV+aPN3CcJLa//AtN2UzPdHm8/bi1Z5NeHreGmx2O/c3qEa1wOJ89uceapUtRdua5WlevQx/HwvlwRlL8PKy8GLXBhQvXIC/j11g2rKdWCwW7HY7A1vWovr/t3ffcVLW1x7HPyuCYsVuLLEBX0uiMSoqVjBgucYaNTGW5FrxWgIxBnvFiAZRo2iujZBEE02MxigmWLjG3jWxHAtqIqKxABZUkN37x3mGHYbZ3Wd1f/Ps7J7367Wv3Xme2T0/dnXO/Nr5rbxM+2LvtjnDrrmTxqZGdt+0H31XWoZxk5702Ot/lYH9V+HBl95kr7E3s1BDA8N33pQ+iy/Kgy+9yYW3P0YD/os6aNsN2hW7mvvveoiBO2zBTQ9cx6effMbZw8+bd+83k67igCGHAnDMKUcydI8dWLT3otz62I38+frbuHLM+HbHm3jH3ey002Cef+4+Zs36hMMO//G8e488fAcDNt+JVVddmRNHHssLL7zEww9NBODyK8Zz7bW/Y9iwHzB48NbMmfM502fM5JBDh+eKu3CPhRi532CGXfpHGhsb2X3Lr9F3leUZd+v9rL/GSmy/YV8Grr8mDz7/OnuddS0LLbQQw/fajj7ZgoOp783krekfskm/1duIVCX2wj04afgwjhhxCnPnzmXPXYfSd+01uPTKCWywbn8GbbMFjz75DBddMZ6GhgY22ehrnPLjowCY8vq/Oev8X9CwUANNjU0ccsC+862u+rJ+cvp5PPrkM8yY8QE77HEARx1yIHt/e8cO+/nt0ZkOUJK0KXALMAb4Hj5sfoWkWWbW0vrjrwBrAacB/wSWBEYBd0vawMymtxazoTt3sSQtAbyDD0ktsGYvm/S+yszOKbt2FdDXzLbPHt8OvGdmba+bbMEnfzinuD9CgWd6b3f0XwuL/fT7xZ0tPeO2UwuLvfBG3yosdtF6Lr/2gpNT7bTC0sr1/+o7M+1Lx2qLpOuANc1sYNm1C4DvmNlaLX/nAj9nOeBdYDczu7W153brHoaZfSRpDHCGpE+ASUBvYBcz+1nOH/MqMEhSaYXVTDObk6bFIYQi5X2DLWlGW88xsz5tPacNWwGVk2d3AMdLWs3M3sj5c0pjmm1ODHbrhJE5Fe9lHAuMBaYD97bj+8fgq6ueBhYHBgGTO7aJIYTOIP/8xBfrYEhaDGhrHfosM5uFDy9VLrYpPf4K0GbCkNQDXyH6KPBwW8/v9gnDzJqAi7OPyntrVrl2aMXjKcC2qdoXQug88vYwvkTv4QTg9DaeMwo4pY3ntNnQLFlMAPoD25pZmxM03T5hhBBCXjXYY3E+cGkbz5mVfZ4GrFxxr7SCs9Vl/pJ6AdcDGwHb5x2+ioQRQgg5zW1Mu0oqG2qa1eYT3f3AjsBZZdd2Al5vLQFkw143AWvgPYvcuywjYYQQQk6drLz5WOABSaPwkkUD8MoU89ZxSxqADzsdZGaPSFoSuB1YDdgdaJRU6qXMNLNWyzJ09417IYSQW2fauGdmjwJ7ALvii27OBk6u2IOxGCCaJ9I3AbYG1sy+Z1rZR5tFz6KHEUIIOXW2fWtmdhtwWyv3J1O2ZKvycXtFwgghhJw62ZBUzUXCCCGEnBoTT3p3dpEwQgghp+7dv+jmtaRCCCHkF6ukQggh5BIJI4QQQi6RMEIIIeQSCSOEEEIukTBCCCHkEgkjhBBCLpEwQggh5BIJI4QQQi6RMEIIIeQSCSOEEEIukTBCCCHkEgkjhBBCLpEwQggh5BIJI4QQQi5xHkb4QiStA6yXPXzezF4psj2h65G0LfCAmX1ecX1hYKCZ3VtMy7qvSBh1SNKiwP7A+tml54DrzeyTGsReDrga+DbNZwM3SfoL8N9m9l7qNtSKpP3zPtfMruvg2C+R87weM+vfkbE7kXuArwD/qbi+dHavR81b1M1FwqgzkjYG/gIsBTyPv2gfAYyStIuZPZm4Cb/EE9VOwP3Zta2AS7N730kVWNKSwEnADsCKVAypmtlXOzjkbyoeN9GcJMuvAXRowqgSu2Yk/S3vc81saMKmNFA9aS4NzEoYN7QgEkb9uQJ4AjjQzGYASOoDTMjubZ44/s7AzhXDAZMkHQZMTBz7GmAb4HfAWyQ+MdPM5iUkSdsDF+MJ674s9jbAOcDwBLHP7Oif2Q5Ty75uAPYEPgQeza5tBiwJ3JQiuKRrsi+bgEsklfecewCbAI+niB1aFwmj/mwIbFZKFgBmNkPSycAjNYg/HXi3yvX3gJmJY++IJ6v723xmx7sIGGFmd5Vdu03Sp8Al+N+lSzCzH5a+lnQ28Gd8uHFOdq0ncBXwZqImrJ59bgBWAWaX3ZsNTAbGJIodWhEJo/68AvSpcr0P8GoN4o8BzpV0gJl9BCBpCeBs4OeJY79J+qTUEjH/O++SqUC/5MGlg/F5qzWAXuX3zGzthKEPBQaXkkUWb46k0fg8wskdHdDMhgBIuhY4zsw+6OgY4YuJhFF/jgMuknQ88FB2bQvgfODYGsTfBRgAvCnpuezaevjwwRKSdio9McH49knAeZIOMrP3O/hnt+VF4HhJh5tZI4CkBuD47F4ykkYAZ+BDctvh7+7743+Hi1PGxoeeVsTny8qtCCyWMnB5Tyd0DpEw6s8d+GTvXVXu3S5p3gMz61XlOV/WG9lHucoXk1Qm4RP8b0t6C5hTfjPxO+0fAbcCO0h6BE+QmwMr4CvGUjocONLMrpN0CHChmU3JhouWTRz7NuBKSUcCD2bXBgKXZfeSyRLyQcBQYCUWXOQwOGX8sKBIGPXn0CKDF/yubwKwMb4aK/mkdzkzu0dSP+B/8B5VA76S6XIzSzWWX/JVmlekfYq/6wf/fTwAHJMw9hHAtcCdzP/7vhUYljAueK/5R1ns16jh3ztUFwmjzpjZr4puQ4GGAjuZ2d+LCG5m04BTCgj9Dj5H9Treu9sYeBpYFeiZMnC2uGJPSX1pTpTP1mij5oHA/mZ2Yw1ihRwiYdQpSYtRfS/ClMRxa70XotxUoNZzF/PIx/uOAPoCh5vZW5J2A143s6cThr4XT5ZPA78HxkraEdgeH6JMzsxeljQdeN/MavVOvye+hDx0EpEw6oyk9fDJzwEVt0qbnFLvfq3pXogKJ9M86T29hnGRtA3wN3wIaGuaJ3zXBw4G9k4Y/hhgkezr0cBc/G/wG3x1WjKSegCn4QsqlsQn26dIOg941cx+mTD8r/Hf6/kJY4R2iIRRf8bja9H3pvYv2FDsXohz8VIRb0uayoKT3ilLZJwLnGVmP5P0Ydn1u/F5jWQq9tw0ARdkH7XwUzwhHovPHZU8ic8vpEwYM4GfShoIPMX8+zEws3MTxg5VRMKoP18DvmlmVlD8IvdCFFYuA9gIf+Gs9Da+UioZSa0O85nZvxKGPxhfoXWHpHFl1/+B9zZSOgj4AP/db1RxrwlP4qGGImHUnyfwd9lFJYzC9kIUXC7jU7yGUaX++KR0Sq/Rek8y5TDkV6m+bPpzoHfCuJjZWil/fmi/SBj15whgnKSL8Hd5lcMyKd9tQrF7IYp0O3CipO9mj5skLY/Xkvpz4tjbVDzuiddTOgo4MXHs1/B3969XXB8CvJAysKQbgKcqh54kjQS+YWbfrf6dIZVIGPVpGbzwW/m7zlpNehe2F0JSY2vxzCzlv/0EvBTGa8CiwM3A2ng5lqRLbVuYL5os6V/AD4AbEoYfB1yc1cwC6Jft5h9FgqKLFbaj+rDTRLziQaixSBj1ZwLwMbAvxUx6F7kX4iDm//eW3mnvAyQdrjKz/0jaBPgusCm+nPhi4Ldm9lnK2K14HF8EkYyZ/SI7A+VP+BDURHx47lwzuzZlbHwI8KMq12fhb5pCjUXCqD/r45PeSYcDWlHYXggzqzbpPV7S08Ag4PLE8T/FX6DHp4yTR1Y24xBgWuI4i+FLd0cDG+CJ8lkz+zhl3Mwr+NDXyxXXh1CbQpuhQiSM+vMUXlenqIRR2F6IVtwNXJg6iKRB+J6IvsAuZvZGVtvpFTObnDBu5el7DTQX/zssYdyFyVYpmdmzwGOpYrVgHDA6O2FyEv472BEvxNjhVXJD2yJh1J+zgQslnQE8w4KTzqnrGhW5F6IlO5N4qa+kPYHrgd/iK6NKhR174/MbkxOGr+xZNeLHlt5jZskq5ZrZ55L+TUFHoZrZZZJWxOdLSqXzPwPGmNkvimhTdxcJo/6UKoTeQjGT3p3p6NDSATvrkr7G0ynA0WZ2laR9y64/gC81Tqbg5cRjgNOz80+SnxlfycxOz87e2CC79FyNhsNCFZEw6s+gIoN3oqNDwd9pPwYcW3ESXgrr4lVTK00ncYlxSYsAlCbXJa0K7IG/eN6TMjawO16GZqqk5/EFF/MkPtO7FGMWzcfDhgJFwqgzZvZ/RbdBUi9gJ/ykuavMbKakNYEZ5WUsOlrBpdWn40Nxr1Vc34jqJ/F1pJvx1UmXZKcbPoLPXywh6TAzG58wdrXzT0I3FQmjDpVVTe0HHFbDqqmlMhWTgNXwgnh/wucPfoTvTzgyYezSfEFLB+qk3DT4R2CUpN2zx02S1gfOwyvIprQJXtMJvGfxIbAWcAAwgoSrtuLUu1AuEkadKbhqKsBYfKXWhsC7ZddvIW0hOvBVM3vglXKnUts9KCcBf8FrRy2CD4X1wYepUg/TLUXzUuYdgJvNbLakO4FLEscGQNKG+BuUiWY2Kxsmm1M6rjZ0D5Ew6k9hVVMz2wCDzOyz8uNg8XXxqyaOvRuwn5lVTn4nl020DpK0Pc0b9x4zs7trEH4qsKGkaXjvqvSuvw++aigZScvivcht8ATdD5iCH9H6Ad7DCd1EJIz6U1jV1ExvKspMZ1bAdwCnNJsF5xBqKttvMbnGYa/Gl/ROwxNEaaJ7AOn34/wcLzS4JvBs2fU/4CuoQjeyUNtPCZ1MkVVTwYfCvlf2uDQsdBx+MlxKl+FzN4WQtIekeyW9m338PdufkVRWfO+HwBXA1mZW2vvSSPP+hFSGAidUKWr5Il7JNnQj0cOoP0VWTQUfy58saV38v58Ts/Ht9YCBHR1M0v9WXNpH0g5UP1Dn8I6OX9aO4fjJbzfgcygN+L/395JGmlnSneZmdlOVa9ekjJlZFl8hVmlJPGGFbiQSRv0prGoqgJk9Lmlz4Cd4rZ+t8QngHySqb9Wv4vFT2ec1Kq6nngA/HhhRscP4MkkPASNJXJokK9OxGf7v7lV+z8wmJAz9BL6EelzF9YOBhxPGDZ1QJIz6sy6wJbAX81dNvQH4JumHhTCz52ieeE0dq9CNimWWxvdCVJqIL61NRlI/fId/X5oTYwP+Dr8Rr2CcypnAzZJWw6sIHChpA3xD3+CEcUMnFHMY9eceYFEzG29mR5vZUdnQxCI0T4YmI2luVt+n8vpykuamjl+gO/Dx/Eo74sucUxoLPAcsj5f2Xh/v2T2BL7NNxswmAd/G36Q04vtBVgZ2LOhc91Cg6GHUn1LNqEpL4y8mtYhfTS8qChF2MfcCZ0kaADyUXdsC2DW7vn/piWZ2XQfH3hz4lpm9L6kpi/GApBOBi/CNfclkS4drsXw4dHKRMOqEpNIEZxNeIqK8EFwP/EXj8YTxDyqLv6+kDyriD8LnNLqqi7LPB2Uf1e6B/346OmH0xPc8gG+WXBk/0/1VfLFBMpKmAJuZ2XsV1/sAT3ThI3lDFZEw6sfq2edShdbyFUKz8b0BKdfFX1329diKe7PxF68uu4nLzIocvn0BTwyv4pP+x2TnqR8L/Dtx7DWpXgF5EdJv1AydTCSMOmFmQwAkXQscZ2YftPEtHR2/Zxb/Vfwd57ttfEvoOBfTvCnzLOCv+JzGbOD7KQJK2rbs4ZaSypfW9sDnc6IoYTfT0NRU6yOhQ72TtAy+1LLaEs+zCmlUYpK+D0w3s9uzx2cBw/Ddz983s9QVa8vb0hvvcbxeOVTUgTEamX9FVqWPgKNaODY3dFHRwwjtImkzfMVQA14U7x38uNBZeOmKLpkw8A2LIwAkbYyvFjoNT5w/Z/7d7x1K0g3AU9mOb7KDjJ6QNFLSxma2X4Kwq+N/43/hy7XLqwjMBt41s3i32c1EwgjtdQFe6vtIvKz5VvgLyHUsOLfRlayBTzSDF0G8xcxGZ6cAVtuf0ZG2w4tOVpqIl2TpcGU9plh6H+aJ/xhCe30DGJuVtW4EepnZG/g77moval3FHHyiF2B7mpeZTsd7WiktjQ8BVZoFLJMysKQhkrYqe3yYpEcljZe0ZMrYofOJhBHaay7NK7T+Q/PqrXdZsFxHV/IwcGq2vHhrfFgO/CCjaYljvwIMqXJ9CL5yKqXz8Q2DSOqPF4B8DK8ycEHi2KGTiSGp0F7P4L2MV/ANbCdJWgg4jOYhm65oBF5ifFfgTDN7Lbu+N80b+VIZB4yWtCh+2mETvsP8DODkxLHXAf6Zfb0ncKeZDZO0JXBj4tihk4mEEdprFLBE9vWpeI2jifik6HeKalRqWf2sjarc+il+XkTK2Jdl5VhG0VzO/DNgTEUxxFRKk9vb0VwGZSqwXA1ih04kltWGLy07lW16rJpJS9JiwAbZw+eyUwBTx7wPeBC4FU8W3zSz57J5jevNLM7E6EaihxG+NDN7v+1n1R9JL5GzbLqZ9U/cHMxsFvBo6jgVRuLntY8Ars56WuAFCWvdllCwSBghtKx8U9oi+JnpLwL3ZdcG4uXmL61xu2rGzO6TtAKwlJnNKLt1JZC8hxM6lxiSCiEHSeOAj83sJxXXRwNLm9mRxbQshNqJHkYI+eyHnwlR6Wp8yW2XSRjZsbgjzOyjKkfkziflsbih84mEEUI+PYD++JBUORXQltT60fzaUHlEbrkYnuhmYkgqhBwkXY7vQzgJXzXUhJdFOQe42cyGFdi8EGoiehgh5HMc8Cm+07kXXphvNnAFvpIohC4vehghtEO2F2IdPGG8nC117bJamcNowhPoi8ANZvZOC88LXUgkjBBCiyTdA2yMHxNbKv3SHy/G+CI+h9MIbF22RyN0UTEkFUIOkhrws7yHAitRUbjTzAYX0a4auBGvinuAmU2HeQdoTQBuB34N3ICXLNmlqEaG2ohqtSHkcz5wFbAs8BpefLH8o6s6ATixlCwAsq9PAUaa2UfAmcAmBbUv1FD0MELI50BgfzPrbhVaV8KHoyr1pPmc8beBxWvWolCY6GGEkE9P4ImiG1GAvwOXSlqrdEHS2sDF2T3IzhcvoG2hxiJhhJDPr/GzL7qbw4HewMuS3pb0FvASsFh2D3ykoque5R7KxCqpEHKQdCZwNP6u+imaTx0EwMy68vG0SBqC9yTAS6vfWWR7QjEiYYSQg6TWjkJtMrO1a9aYEAoSCSOE0CpJPwSOxTcsbmRmr0o6AXjFzP5YbOtCLcUcRgihRZIOB8YAN+ET/w3ZrXfwIbrQjcSy2hBaIOkkYKyZfZJ93aIuPIdxDHCEmd2Y9SpKHgdGF9SmUJBIGCG07DDgl8An2dctaQK6asLoCzxS5frHwFI1bksoWCSMEFpgZmuVPfx6tqu5u5mGJ43KfRZbAlNq35xQpEgYIeQzXdKjwF3A3cADZvZZwW2qhQnAGEn74j2p3pJ2wYejxhbaslBzkTBCyGcoMAgYjNdXmivpITx53G1mDxTZuITOBtYEnscnvJ/Jrl+LT4aHbiSW1YbQTpIWB7YB9sFrTPUwsx7FtqrjSeoJvIEnyY+BTfGVlY+bWVcuuBhaED2MEHLKDk/aFn8B/RbwdeAfeC+jyzGzOZLmAnPN7DW8Sm/oxiJhhJCDpHuBAcDLwGT8LO97yst+d1FX4Zv2jiq6IaF4kTBCyGcA8AHwIHAfcF83SBYAqwD7SBqM7734uPymmR1e9btClxQJI4R8lga2woejjgV+JelFmie9bymycQmtQ3NZ91Uq7sUEaDcTk94hfAGSVgdOAw6mi056h1Apehgh5CBpUXxl1A54L2Nj4FN8X8ZdBTYthJqJhBFCPjOARuBh4FZgOPCwmX1eaKtCqKFIGCHk81/A/Wb2adENCaEoMYcRQgghlzgPI4QQQi6RMEIIIeQSCSOEEEIukTBCCCHk8v8BwWBrbB3iUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = train[[\"temp\",\"atemp\",\n",
    "                  \"hum\",\"windspeed\",\n",
    "                  \"casual\",\"registered\",\n",
    "                  \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和温度高度相关\n",
    "目标cnt与温度正相关、湿度和风速负相关"
   ]
  }
 ],
 "metadata": {
  "_change_revision": 0,
  "_is_fork": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
